WS07 - ReviewModel1

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Wed, 25 Nov 2009 11:43:00 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga.htm/, Retrieved Wed, 25 Nov 2009 19:44:36 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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8.00 96.80 8.10 114.10 7.70 110.30 7.50 103.90 7.60 101.60 7.80 94.60 7.80 95.90 7.80 104.70 7.50 102.80 7.50 98.10 7.10 113.90 7.50 80.90 7.50 95.70 7.60 113.20 7.70 105.90 7.70 108.80 7.90 102.30 8.10 99.00 8.20 100.70 8.20 115.50 8.20 100.70 7.90 109.90 7.30 114.60 6.90 85.40 6.60 100.50 6.70 114.80 6.90 116.50 7.00 112.90 7.10 102.00 7.20 106.00 7.10 105.30 6.90 118.80 7.00 106.10 6.80 109.30 6.40 117.20 6.70 92.50 6.60 104.20 6.40 112.50 6.30 122.40 6.20 113.30 6.50 100.00 6.80 110.70 6.80 112.80 6.40 109.80 6.10 117.30 5.80 109.10 6.10 115.90 7.20 96.00 7.30 99.80 6.90 116.80 6.10 115.70 5.80 99.40 6.20 94.30 7.10 91.00 7.70 93.20 7.90 103.10 7.70 94.10 7.40 91.80 7.50 102.70 8.00 82.60

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Wman[t] = + 9.66287356011315 -0.0238024768072979Ecogr[t] + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 9.66287356011315 0.900642 10.7289 0 0 Ecogr -0.0238024768072979 0.008571 -2.7771 0.007374 0.003687 Multiple Linear Regression - Regression Statistics Multiple R 0.342588997638699 R-squared 0.117367221303089 Adjusted R-squared 0.102149414773832 F-TEST (value) 7.7124926695213 F-TEST (DF numerator) 1 F-TEST (DF denominator) 58 p-value 0.00737384572121003 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.623206484191725 Sum Squared Residuals 22.5264066724394 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8 7.35879380516671 0.641206194833285 2 8.1 6.94701095640045 1.15298904359955 3 7.7 7.03746036826819 0.662539631731815 4 7.5 7.18979621983489 0.310203780165108 5 7.6 7.24454191649168 0.355458083508323 6 7.8 7.41115925414276 0.388840745857237 7 7.8 7.38021603429327 0.419783965706725 8 7.8 7.17075423838905 0.629245761610946 9 7.5 7.21597894432292 0.284021055677080 10 7.5 7.32785058531722 0.17214941468278 11 7.1 6.95177145176191 0.148228548238087 12 7.5 7.73725318640274 -0.237253186402744 13 7.5 7.38497652965473 0.115023470345265 14 7.6 6.96843318552702 0.631566814472979 15 7.7 7.1421912662203 0.557808733779704 16 7.7 7.07316408347913 0.626835916520868 17 7.9 7.22788018272657 0.672119817273432 18 8.1 7.30642835619065 0.793571643809348 19 8.2 7.26596414561824 0.934035854381754 20 8.2 6.91368748887024 1.28631251112976 21 8.2 7.26596414561824 0.934035854381754 22 7.9 7.0469813589911 0.853018641008896 23 7.3 6.9351097179968 0.364890282003196 24 6.9 7.6301420407699 -0.730142040769903 25 6.6 7.2707246409797 -0.670724640979705 26 6.7 6.93034922263534 -0.230349222635344 27 6.9 6.88988501206294 0.0101149879370625 28 7 6.97557392856921 0.0244260714307898 29 7.1 7.23502092576876 -0.135020925768758 30 7.2 7.13981101853957 0.0601889814604341 31 7.1 7.15647275230467 -0.056472752304675 32 6.9 6.83513931540615 0.0648606845938478 33 7 7.13743077085884 -0.137430770858836 34 6.8 7.06126284507548 -0.261262845075483 35 6.4 6.87322327829783 -0.473223278297829 36 6.7 7.46114445543809 -0.761144455438088 37 6.6 7.1826554767927 -0.582655476792703 38 6.4 6.98509491929213 -0.585094919292129 39 6.3 6.74945039889988 -0.44945039889988 40 6.2 6.96605293784629 -0.766052937846291 41 6.5 7.28262587938335 -0.782625879383354 42 6.8 7.02793937754527 -0.227939377545266 43 6.8 6.97795417624994 -0.177954176249940 44 6.4 7.04936160667183 -0.649361606671834 45 6.1 6.8708430306171 -0.7708430306171 46 5.8 7.06602334043694 -1.26602334043694 47 6.1 6.90416649814732 -0.804166498147317 48 7.2 7.37783578661255 -0.177835786612545 49 7.3 7.28738637474481 0.0126136252551865 50 6.9 6.88274426902075 0.0172557309792519 51 6.1 6.90892699350878 -0.808926993508776 52 5.8 7.29690736546773 -1.49690736546773 53 6.2 7.41829999718495 -1.21829999718495 54 7.1 7.49684817064904 -0.396848170649035 55 7.7 7.44448272167298 0.255517278327021 56 7.9 7.20883820128073 0.69116179871927 57 7.7 7.42306049254641 0.276939507453589 58 7.4 7.4778061892032 -0.0778061892031964 59 7.5 7.21835919200365 0.281640807996351 60 8 7.69678897583034 0.303211024169662 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.121785386787318 0.243570773574637 0.878214613212682 6 0.0482542743810077 0.0965085487620153 0.951745725618992 7 0.0172541545434744 0.0345083090869488 0.982745845456526 8 0.00585317028991931 0.0117063405798386 0.99414682971008 9 0.00386792553146366 0.00773585106292731 0.996132074468536 10 0.00197414236000052 0.00394828472000104 0.99802585764 11 0.0072408102533972 0.0144816205067944 0.992759189746603 12 0.00414850298133946 0.00829700596267893 0.99585149701866 13 0.00190628899466354 0.00381257798932708 0.998093711005337 14 0.000862503251589 0.001725006503178 0.99913749674841 15 0.000378300013743677 0.000756600027487354 0.999621699986256 16 0.000170905262863513 0.000341810525727026 0.999829094737136 17 0.000120847192389412 0.000241694384778824 0.99987915280761 18 0.000214259986826536 0.000428519973653071 0.999785740013173 19 0.00060408894734389 0.00120817789468778 0.999395911052656 20 0.00233941189718986 0.00467882379437973 0.99766058810281 21 0.00584857805943207 0.0116971561188641 0.994151421940568 22 0.00785841516550374 0.0157168303310075 0.992141584834496 23 0.0129086370286779 0.0258172740573558 0.987091362971322 24 0.0395209898875724 0.0790419797751449 0.960479010112428 25 0.156897225433393 0.313794450866786 0.843102774566607 26 0.282894270366896 0.565788540733793 0.717105729633104 27 0.330203914449691 0.660407828899382 0.669796085550309 28 0.339483349126674 0.678966698253347 0.660516650873326 29 0.312701198088229 0.625402396176457 0.687298801911771 30 0.287511192283293 0.575022384566586 0.712488807716707 31 0.263706789472349 0.527413578944699 0.736293210527651 32 0.286965280095144 0.573930560190289 0.713034719904856 33 0.26615271905395 0.5323054381079 0.73384728094605 34 0.261760855112603 0.523521710225205 0.738239144887397 35 0.298643433495721 0.597286866991441 0.701356566504279 36 0.358738009454688 0.717476018909376 0.641261990545312 37 0.363541481662976 0.727082963325952 0.636458518337024 38 0.37113706163833 0.74227412327666 0.62886293836167 39 0.365330103064557 0.730660206129115 0.634669896935443 40 0.378534222463802 0.757068444927605 0.621465777536198 41 0.401037076694803 0.802074153389605 0.598962923305197 42 0.343246881890372 0.686493763780744 0.656753118109628 43 0.295924651955832 0.591849303911664 0.704075348044168 44 0.26363311443066 0.52726622886132 0.73636688556934 45 0.238869592112753 0.477739184225507 0.761130407887247 46 0.358020546570695 0.71604109314139 0.641979453429305 47 0.33323899923475 0.6664779984695 0.66676100076525 48 0.252515951564903 0.505031903129806 0.747484048435097 49 0.184704526638826 0.369409053277651 0.815295473361174 50 0.144715450787085 0.289430901574171 0.855284549212915 51 0.116928407049026 0.233856814098052 0.883071592950974 52 0.461422434030424 0.922844868060848 0.538577565969576 53 0.928282892788987 0.143434214422026 0.071717107211013 54 0.965930471688251 0.0681390566234972 0.0340695283117486 55 0.901895543659822 0.196208912680357 0.0981044563401784 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.215686274509804 NOK 5% type I error level 17 0.333333333333333 NOK 10% type I error level 20 0.392156862745098 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/10skxj1259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/10skxj1259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/1smqb1259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/1smqb1259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/2u57z1259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/2u57z1259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/3hhvp1259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/3hhvp1259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/4s2ri1259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/4s2ri1259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/5d1ed1259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/5d1ed1259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/69ae51259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/69ae51259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/7tvz01259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/7tvz01259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/8f84a1259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/8f84a1259174576.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/9kibk1259174576.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259174661rk8ftst492o9qga/9kibk1259174576.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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